Traditional models, like Generalised autoregressive conditional heteroscedasticity (GARCH)-based models) are widely used for volatility forecasting [1]}, [2]}. [3]} and [4]} study volatility forecasting for Bitcoin using GARCH and its variants. [5]} concluded, however, that the predictive ability of GARCH is not good in the context of unusually high volatility, and performs better when volatility is relatively low. [6]} brings up one of GARCH's major limitations where “its memory is sometimes not long enough to capture the persistence of some shocks that are observed to last for a very long time”.
[7]} propose a time-varying mixture model, which includes an accelerating generalized autoregressive score (aGAS) technique into the Gaussian-Cauchy mixture (TVM)-aGAS model for forecasting Value-at-Risk for cryptocurrencies.
Recently, however, many researchers have turned to ever more powerful deep learning models for financial time series prediction.
